Integrated service centre support

ABSTRACT

A curator captures input data corresponding to service tasks from an external source. Further, a browser extension collects intermediate service delivery data for the service tasks from the external source. Subsequently, a learner stores the input data and the intermediate service delivery data as training data. Then, a receiver receives a service request from a client. The service request is indicative of a service task to be performed and information associated with the service task. Further, an advisor processes the service request to generate an intermediate service response. Thereafter, the advisor determines a confidence level associated with the intermediate service response and ascertains whether the confidence level associated with service response is below pre-determined threshold level. If the confidence level is below a pre-determined threshold level, the advisor automatically generates a final service response corresponding to service request based on training data.

PRIORITY

The present application claims priority under 35 U.S.C. 119(a)-(d) toIndian patent application number 201741006558, having a filing date ofFeb. 23, 2017, the disclosure of which is hereby incorporated byreference in its entirety.

BACKGROUND

Efficient and customer-oriented service delivery is essential forsuccess of any organization. Typically, an input system, such as aservice representative, delivers services in various service deliveryenvironments, such as call centers, business process outsourcing,infrastructure outsourcing, application outsourcing, and service desks.During recent years, providing automation such as through roboticprocess automation (RPA), smart process automation (SPA), andintelligent process automation (IPA) has gained substantial momentum.Currently, the automation approaches are categorized into self-assistdelivery models and agent-assist delivery models. The self-assistdelivery models are fully automated models where a system or a machinegenerates a solution or a recommendation for a client without any manualor human intervention. Further, the agent-assist delivery models aresemi-automated models where some aspects are automated while otheraspects may be handled by a human agent. A technical problem that arisesin service delivery is that while self-assist delivery models andagent-assist delivery models may have many advantages individually,however, for most of the aspects, the delivery models are mutuallyexclusive wherein capabilities of one delivery model cannot be used bythe other delivery model. The present disclosure provides a technicalsolution to the problem that can intelligently assist systems for speedyand effective resolving of customer queries.

BRIEF DESCRIPTION OF DRAWINGS

Features of the present disclosure are illustrated by way of examplesshown in the following figures. In the following figures, like numeralsindicate like elements, in which:

FIG. 1 illustrates a network environment implementing a system,according to an example embodiment of the present disclosure;

FIG. 2 illustrates a block diagram of the system, according to anexample embodiment of the present disclosure;

FIG. 3 illustrates a block diagram where intermediate information iscollected using a browser extension according to an embodiment of thepresent disclosure;

FIG. 4 illustrates a process for incorporating external system inputs inthe system generated recommendation according to an embodiment of thepresent disclosure;

FIG. 5 illustrates an integrated solution model for holistic integrationof external system inputs and the system generated recommendations,according to an example embodiment of the present disclosure;

FIG. 6 illustrates a block diagram of an integrated cognitive techsupport system, according to an embodiment of the present disclosure;

FIG. 7 illustrates an exemplary system for resolving customer queries,according to an embodiment of the present disclosure;

FIG. 8 illustrates a ticket categorization process, according to anexample embodiment of the present disclosure;

FIG. 9 illustrates a knowledge extraction process, according to anexample embodiment of the present disclosure;

FIG. 10 illustrates a hardware platform for implementation of thesystem, according to an example embodiment of the present disclosure;and

FIG. 11 illustrates a computer-implemented method depictingfunctionality of the system, according to an example embodiment of thepresent disclosure.

DETAILED DESCRIPTION

For simplicity and illustrative purposes, the present disclosure isdescribed by referring mainly to examples thereof. The examples of thepresent disclosure described herein may be used together in differentcombinations. In the following description, details are set forth inorder to provide an understanding of the present disclosure. It will bereadily apparent however, that the present disclosure may be practicedwithout limitation to all these details. Also, throughout the presentdisclosure, the terms “a” and “an” are intended to denote at least oneof a particular element. As used herein, the term “includes” meansincludes but not limited to, the term “including” means including butnot limited to. The term “based on” means based at least in part on.

The present subject matter describes systems and methods for enablingholistic integration of various delivery models in a service deliveryenvironment. The system addresses and assists systems for resolvingvarious types of customer/vendor queries. In an example embodiment ofthe present disclosure, the system may include a receiver and anadvisor. The receiver and the advisor may be in communication with eachother to perform the functionalities of the system. The system may alsobe referred to as an integrated service centre support system.

Further, in an example embodiment, the system may be communicativelycoupled to a database through one or more communication links. Thedatabase may store input data corresponding to a plurality of servicetasks and intermediate service delivery data. The input data includesdata, such as question/answer pairs and dialog traces corresponding toeach of the plurality of service tasks. For instance, a question/answerpair for a mobile device ‘X’ support may be a) Question—“Does the mobiledevice ‘X’ support a fast charging tech”? b) Answer—“Yes, it does”.Another example of a question/answer pair for the mobile device ‘X’support may be a) Question—“What charger does the fast charging”? b)Answer—“You need a charger that is 3 amps at 5 volts to take fulladvantage”.

In an example, the input data and the intermediate service delivery datamay be collected from external sources, such as external systems.Further, examples of service tasks include, but are not limited to,technical support and classification of advertisements against a set ofpolicies. Whenever the system receives a service task to be performedfrom a client, the system retrieves the input data and the intermediateservice delivery data from the database.

According to an example embodiment of the present subject matter, thesystem may initially be trained with the input data and the intermediateservice delivery data corresponding to the plurality of service tasksusing machine learning techniques. The input data and the intermediateservice delivery data may also be referred to as training data. Further,the training of the system may or may not be performed in real-time.

For the purpose of processing a service task, the receiver of the systemof the present subject matter may receive a service request from aclient. In an example, the service request is indicative of the servicetask to be performed and information associated with the service task.Further, the service task to be performed may be one of the plurality ofservice tasks whose data is stored in the database. Then the servicetask received from the client is processed through the system. Theservice task is processed based on the training of the system.

Once the service request is received from the client, the advisor of thesystem processes the service request to generate an intermediate serviceresponse corresponding to the service request. The service request maybe processed based on pre-stored information in the system. In anexample, the intermediate service response may be understood as asolution to the service request. Further, the intermediate serviceresponse may be generated as an intermediate recommendation.Subsequently, the advisor may determine a confidence level associatedwith the intermediate service response. The confidence level associatedwith the intermediate service response may be determined based on aconfusion matrix.

According to an example implementation, the advisor may furtherascertain whether the confidence level associated with the serviceresponse is below a pre-determined threshold level. On ascertaining thatthe confidence level is below the pre-determined threshold level, theadvisor automatically may generate a final service responsecorresponding to the service request based on the training data. In aninstance, the final service response is generated as a finalrecommendation. Further, as described above, the training data includesthe input data and the intermediate service delivery data.

The system of the present disclosure may offer efficient, reliable, andaccurate processing of the service request. The system incorporatesexternal systems' inputs i.e., knowledge from curated question/answerspairs and dialog traces from the external systems to generate finalrecommendations. The system gathers the knowledge from unstructured andsemi-structured sources and assists adjudicative systems for takingdecisions in resolving various types of service requests. Such aholistic integration various delivery models may lead to better,efficient, and holistic service delivery

FIG. 1 illustrates a network environment implementing a system 100,according to an example embodiment of the present disclosure. The system100 may also be referred to as an integrated service centre supportsystem. In an example embodiment, the system 100 may be used forprocessing service requests received from clients to generaterecommendations. In an example, the service requests may be related totechnical support, classification of advertisements against a set ofpolicies, and other such requests.

In an example embodiment, the network environment may be a publicnetwork environment, including numerous of individual computers,laptops, various servers, such as blade servers, and other computingdevices. In another example embodiment, the network environment may be aprivate network environment with a limited number of computing devices,such as individual computers, servers, and laptops. Furthermore, thesystem 100 may be implemented in a variety of computing systems, such asa laptop, a tablet, and the like.

According to an example embodiment, the system 100 is communicativelycoupled with a database 105 through a network 110. The database 105comprises input data corresponding to a plurality of service tasks andintermediate service delivery data. The input data includes data, suchas question/answer pairs and dialog traces corresponding to each of theplurality of service tasks. The input data and the intermediate servicedelivery data may collectively be referred as training data. In anexample, the input data and the intermediate service delivery data maybe collected from external sources, such as external systems. Further,examples of service tasks include, but are not limited to, technicalsupport and classification of advertisements against a set of policies.The input data may include question/answer pairs and dialog tracescorresponding to each of the plurality of service tasks.

The database 105 may include any other suitable information related tothe plurality of service tasks. Further, the database 105 may beaccessed whenever a service task is to be processed by the system 100.Furthermore, the database 105 may be periodically updated. For example,new data may be added into the database 105, existing data in thedatabase 105 may be modified, or non-useful data may be deleted from thedatabase 105.

In an example embodiment, the network 110 may be a wireless network, awired network, or a combination thereof. The network 110 may also be anindividual network or a collection of many such individual networks,interconnected with each other and functioning as a single largenetwork, e.g., the Internet or an intranet. The network 110 may beimplemented as one of the different types of networks, such as intranet,local area network (LAN), wide area network (WAN), the internet, and thelike. Further, the network 110 may include a variety of network devices,including routers, bridges, servers, computing devices, storage devices,and the like.

According to an example embodiment, the system 100 may include areceiver 115 and an advisor 120. In an example embodiment, the receiver115 and the advisor 120 may be in communication with each other toperform the functionalities of the system 100.

Initially, the receiver 115 receives a service request from a client. Inan example, the service request is indicative of the service task to beperformed and information associated with the service task. In anexample, the receiver 115 may receive the service request in a chat box,virtual assistant environment, or in a question/answer environment.Further, the service task to be performed may be one of the plurality ofservice tasks whose data is stored in the database 105. Upon receipt ofthe service task, the service task is processed through the system 100.In an example, the service task may be to classify text in anadvertisement against a set of policies that are organized as anontology. Further, the information associated with the service task maybe the text corresponding to the advertisement.

Once the service request is received from the client, the advisor 120 ofthe system 100 processes the service request to generate an intermediateservice response corresponding to the service request. The servicerequest may be processed based on pre-stored information in the system.In an example, the intermediate service response may be understood as asolution to the service request. Further, the intermediate serviceresponse may be generated as an intermediate recommendation.Subsequently, the advisor 120 may determine a confidence levelassociated with the intermediate service response. The confidence levelassociated with the intermediate service response may be determinedbased on a confusion matrix. In an example, the confidence level may bedirectly proportional to the ambiguity level of the intermediate serviceresponse. More ambiguous the intermediate service response is, theconfidence level will also be low.

According to an example implementation, the advisor 120 may furtherascertain whether the confidence level associated with the serviceresponse is below a pre-determined threshold level. The pre-definedthreshold level can be considered to determine which of therecommendation output needs inputs from the external system.

On ascertaining that the confidence level associated with the serviceresponse is above the pre-determined threshold level, the advisor 120may then provide the intermediate service response to an externalsystem, for final validation for accurate recommendations generation.According to said example, the validation may be performed on randomlyselected recommendations. The advisor 120 may then finalize theintermediate recommendation based on validation from the externalsystem.

Further, on ascertaining that the confidence level is below thepre-determined threshold level, the advisor 120 may extract the trainingdata from the database 105 and generate a final service responsecorresponding to the service request based on the training data. In aninstance, the final service response is generated as a finalrecommendation. The training data includes external system's inputs. Inan example, the external system's inputs are solicited for clarifyingconfusing scenarios.

According to an example embodiment, the system generated intermediateresults are provided to an external system and inputs are solicited fromthe external system. In one example, after the advisor 120 performsinitial analysis, classification, and reasoning, the results aredisplayed to the external system. The external system can then provideinputs to disambiguate any confusions or correct inaccuraterecommendations.

As described above, the training data includes the input data and theintermediate service delivery data. Further, the input data includesquestion/answer pairs and dialog traces corresponding to each of theplurality of service tasks. The question/answer pairs bootstrap theability of the advisor 120 to either answer questions in thequestion/answer environment or conduct a dialog in the chat box or thevirtual assistant environment.

In an example, every new question answered by an external system can bea valuable resource. Such a new question and its associated answer aremade part of the training data. If the client asks the same questionthen the system 100 can answer automatically. In effect, knowledge ofthe external systems is transferred to the system 100, thus making thesystem 100 more efficient. The manner in which system 100 generates therecommendation for the service task is further described in detail inconjunction with FIG. 2.

FIG. 2 illustrates a block diagram of the system 100, according to anexample embodiment of the present disclosure. As described above, theArtificial Intelligence (AI) based system 100 may include the receiver115 and the advisor 120. In an example embodiment, the system 100includes a curator 205, a browser extension 210, and a learner 215.Further, in an example embodiment, the curator 205 may be incommunication with the browser extension 210, and the browser extension210 may be in communication with the learner 215.

In an example embodiment, the curator 205 may capture input datacorresponding to a plurality of service tasks from an external source,such as an external system. The input data includes question/answerspairs and dialog traces corresponding to each of the plurality ofservice tasks. In an example, the curator 205 collects and curatesrecommendations for the plurality of service requests from the externalsystem. The recommendations provided by the external system are finalrecommendations with respect to the service requests. By curating suchfinal recommendations, insightful data can be generated and used fortraining the system 100.

Further, the browser extension 210 may collect decisions made by theexternal system during the process of providing recommendations. Suchintermediate decisions may provide insight on how the decision-makingprocess evolves leading to the recommendations. The manner in which thebrowser extension 210 collects decisions made by the external system isfurther described in detail in conjunction with FIG. 3.

Thereafter, the learner 215 stores the input data and the intermediateservice delivery data or the intermediate information as training datain the database 105. In an example, the training data is remotely storedin the learner 215. Further, the collected data may be used tocontinuously train and test the system 100 on how to come to aparticular final recommendation and what steps or decision routes shouldbe taken to come to a final recommendation. The advisor 120 can interactwith the database 105 to generate a more accurate final recommendation.

According to an example implementation, the receiver 115 receives aservice request from a client. The service request is indicative of aservice task to be performed and information associated with the servicetask. In an example, the service task may to classify text in anadvertisement against a set of policies that are organized as ontology.Further, the advisor 120 may process the service request to generate anintermediate service response corresponding to the service request. Theintermediate service response is generated as an intermediaterecommendation. In an example, the advisor 120 may generate theintermediate service response by performing analysis, classification,and reasoning methodologies on the service request.

Subsequently, the advisor 120 may determine a confidence levelassociated with the intermediate service response. In an example, theadvisor 120 determines the confidence level associated with theintermediate service response based on a confusion matrix. Then theadvisor 120 may ascertain whether the confidence level associated withthe service response is below a pre-determined threshold level. Onascertaining that the confidence level is below the pre-determinedthreshold level, the advisor 120 may extract the training data from thedatabase 105. In an example, if ambiguity exists in the generatedintermediate service response, then the advisor 120 solicit additionalinput from the external system for clarifying confusing scenarios,following which final recommendations are made by the advisor 120.

According to an example embodiment, the advisor 120 may automaticallygenerate a final service response corresponding to the service requestbased on the training data. The final service response is generated as afinal recommendation. Thus, the final recommendation is made with highenough confidence level. The manner in which the advisor 120incorporates external system inputs in the process of producingmachine-generated recommendation is further described in detail inconjunction with FIG. 4.

The present disclosure thus provides a collaborative model for theexternal system to provide assistance to machine generatedrecommendations. In the present disclosure, the system 100 enablesexternal systems to participate in the process of generating machinegenerated recommendations for service tasks. This may result intoimproved results in comparison to individual results generated by thesystem alone. Further, the results curated by the external systems arecontinuously collected for training the system 100. As a result,continuous and incremental learning is achieved. Furthermore, theintegration of various delivery models enables the overall support inreaching better performance in comparison to system alone approaches.Also, the proposed system is scalable and domain independent that can beused across domains and can intelligently assist external systems forspeedy and effective resolving of service requests by using auto-learnedand process specific knowledge-base and reasoning techniques. Inaddition, the system can also address some of the service requestsautomatically without external systems in the loop. Thus, the process ofgeneration of recommendations for the service requests is performed bythe system 100 in an efficient and an accurate manner.

FIG. 3 illustrates a block diagram 300 where intermediate information iscollected using a browser extension 210 according to an embodiment ofthe present disclosure. The browser extension 210 may be a componentsuch as, for example, a plug in that extends the functionality of a webbrowser.

As can be seen in FIG. 3, intermediate decisions or deliberations madeby the external system are continuously collected by the browserextension 210 without interfering with a primary data path where thefinal recommendations by the external system are being made. The browserextension 210 may be used for collecting intermediate information.Specifically, the browser extension 210, in a browser, may open a taskin a queue while the browser extension 210 performs actual scoring,evaluation, and recommendation. The browser extension 210 then providesthe intermediate information collected from the external system to thelearner 215 via Hypertext Transfer Protocol (HTTP). In an example, thelearner 215 may be a centralized back-end server. The learner 215 thenstores the intermediate information in the database 105 for training ofthe system 100.

FIG. 4 illustrates a process 400 for incorporating external systeminputs in the system generated recommendation according to an embodimentof the present disclosure.

As can be seen in FIG. 4, a text corpus 405 from a service request isprovided as an input to the advisor 120. The text corpus 405 includestext from the service request providing information about a situation.There may be insights that can be generated from the content in the textcorpus 405. In an embodiment, natural language understanding techniquesand natural language architectures may be used to generate the insights,which results in the advisor 120 generating recommendation outputs.

In an example embodiment, confidence intervals are calculated for theeach of the recommendation outputs. Each of the recommendation outputscan have varying confidence levels. Some recommendation outputs may havehigher confidence levels while others may have acceptable confidencelevels. For example, the recommendation output “qualifier” has a lowerconfidence level in comparison to “cosmetic weight loss” recommendationoutput. As another example, the recommendation output “benefits” has alower confidence level in comparison to “income” recommendation output.The advisor 120 then solicits external system's inputs 410 for themachine recommendations for at least the results with lower confidencelevels. A confidence level threshold can be considered to determinewhich of the recommendation output needs inputs 410 from the externalsystem. After inputs 410 from the external system are received, arecommendation is finalized.

FIG. 5 illustrates an integrated solution model 500 for holisticintegration of external system inputs and the system generatedrecommendations, according to an example embodiment of the presentdisclosure.

In an example, during a bootstrapping phase of deployment of a newservice, enabling service for a new product, or starting a new process,a plurality of service requests are received. Further, if it isdetermined that any of the service requests includes insufficientstatistics, then insufficient statistics are gathered from externalsources, such as external systems. Further, the service requests thatare deemed ambiguous or no answers exist for then, may be passed on tothe external systems. Further, recommendations or solutions for suchservice requests are collected from the external systems and arecurated.

As can be seen in FIG. 5, the integrated solution model 500 is used tocurate the recommendations made by the external systems. The integratedsolution model 500 interfaces with knowledge repositories 505 andvarious data sources 510. The knowledge repositories 505 can includesoftware models such as behavior model, ontologies, dictionaries,knowledge graphs, etc. Data from various sources is stored in databases,which can be accessed by the knowledge repositories 505 to enable theintegrated solution model 500 to curate the recommendations made by theexternal systems. In an example implementation, the curated data maybecome a training set for the system 100. Further, machine learningtechniques may be used for transferring the curated data to the system100. Thus, results curated by external systems are collected and madepart of the training set for the system 100.

FIG. 6 illustrates a block diagram of an integrated cognitive techsupport system 600 according to an embodiment.

The integrated cognitive tech support system 600 provides an interfacebetween a user and a computer-based application. The integratedcognitive tech support system 600 includes a dialog manager 605. Thedialog manager 605 controls the flow of dialog. Further, the dialogmanager 605 gathers information from the user, communicates withexternal applications, and communicates information back to the user. Inan embodiment, the dialog manager 605 may be a finite state orgraph-based system, a frame-based system, or any combination thereof.

The dialog manger 605 interfaces with a natural language understandingsystem 610 and a natural language generation system 615. The dialogmanager 605 includes a belief state predictor 620 and an action planner625. The integrated cognitive tech support system 600 further includes asymbolic reasoning engine 630 that interfaces with a decision tree 635,system behavior models 640, and knowledge graphs 645. In an embodiment,the dialog manager 605 interfaces with the symbolic reasoning engine 630to generate an action plan based on the knowledge pertinent to a servicedomain. For example, an action plan can be generated based on knowledgesuch as industry-specific knowledge, client-specific knowledge, or othertypes of knowledge.

FIG. 7 illustrates an exemplary system 700 for resolving customerqueries, according to an embodiment of the present disclosure.

According to an example embodiment, the system 100 employs variousadvanced Natural Language processing (NLP), Information Extraction (IE),and Machine Learning (ML)/Deep Learning (DL) methods to categorizevarious types of customer queries including information fetchingqueries, language analysis and reasoning based queries,computational/arithmetic queries, and instructive and investigativetypes of queries. In an example, the system 100 employs advancedDL-based cascaded ML techniques to identify intents of a ticket query tobe used for resolution for the external systems.

In an example, when a vendor asks for the status of an invoice and sendsa query in an email, then ticket will be created for this query. In thiscase, an external system may respond to the vendor by understanding theemail content, extracting required information related to the invoice indifferent Enterprise Resource Planning (ERP) systems 705, and performinga series of actions and update the status in the template. The system100 assists the external system with required information for updatingthe required status in the ERP systems 705 for future reference.

According to an example embodiment, a ticket categorization component710 of the system 100 reads ticket description included in the email andunderstands the intent of the email with specific ticket category, alsoreferred to as invoice category. Further, the ticket categorizationcomponent 710 employs advanced deep learning techniques, such asconvolutional neural networks (CNN) along with classical machinelearning techniques to classify the given ticket into predefined set ofticket categories/intents/sub-intents with confidence scores.

FIG. 8 illustrates a ticket categorization process 800, according to anexample embodiment of the present disclosure.

Initially, historical corpus 805 of ticket descriptions andcorresponding set of ticket types (intents) and sub-types (sub-intents)which are denoted as labels are fed into the ticket categorizationcomponent 710. The ticket categorization component 710 then performs theticket categorization and splits it into various training, validationand test sets. In case there are any ambiguities in identifying ticketcategories, the ticket categories can be further reviewed by externalsystems. Thereafter, the ticket categorization component 710 prunesvarious spurious and noisy features from the email thread usingpattern-based approach.

Subsequently, the ticket categorization component 710 extracts differenttypes of features ranging from unigram, bigrams to numeric, and alphanumeric from the email description, and performs various preprocessingtasks, such as stemming, stop word removals and lemmatization on theemail descriptions using NLP techniques. In an example, the ticketcategorization component 710 compiles features 810, such as embeddingfeatures (semantic features) from both the email descriptions andsubject using word2vec algorithms. In an example, the embeddingdimensions may vary from 50-300.

The ticket categorization component 710 may then represent each emaildescription with subject into embedding semantic space and this will actas input to the n-layered convolutional deep neural networks. Thelearning processes are optimized with various parameters such as samplebatch size, feature window sizes, optimization techniques, number oflayers, number of iterations, number of semantic features dimensions,etc. In an example, the ticket categorization component 710automatically learns patterns and ticket intents from the historicalcorpus 805 and for a given input email ticket, the ticket categorizationcomponent 710 predicts the correct ticket category/intent with highconfidence scores.

According to the example embodiment, the ticket categorization component710 predicts the ticket categories with high-accuracy. The ticketcategorization component 710 selects the highly confident, mid and lowconfident predictions by varying confidence threshold from 0.9 to 1which can help the external systems to quickly take decisions based onthe confidence level and provide their recommendation. Accordingly, withthe help of minimal features, the system 100 can automatically infervariations of various vocabularies and can handle unseen semanticvariants present in the completely unseen email descriptions.

Continuing with the description of FIG. 7, a knowledge extractioncomponent 715 of the system 100 extracts information from digitized andnon-digitized invoice documents and email query content. In an example,the knowledge extraction component 715 extracts the information based onML/DL techniques, such as Conditional random fields (CRF) and Longshort-term memory deep neural networks. In an example, the knowledgeextraction component 715 reads the ticket description encoded in emailalong with the subject of the email, and understands the intent of theemail with specific invoice (ticket) category. In an example, theknowledge extraction component 715 can read attached invoice copies andemail description, and identify information, such as invoice number,invoice date, various types of amounts, address details, currency andother required information required for providing the resolution.External systems may use this information and provide the resolutionquickly.

The knowledge extraction component 715 uses existing Optical CharacterRecognition (OCR) tools to convert the documents into Hypertext MarkupLanguage (HTML) or textual format useful for various automation tasks.The knowledge extraction component 715 consumes the converted text formachine learning purpose. The knowledge extraction component 715 mayemploy advanced deep learning technique called long short-term memorydeep neural networks (LSTMs) and its variant techniques (Bi-directionalLSTM) with efficient invoice representation to extract key knowledgerequired for aiding the external system to provide quick resolution.Example invoice copy and entities are illustrated in below example.

XYLDEP Bonnect Invoice# IN0006789345 Unit 5, xyz park, ABC Estate,Invoice No: IN00023451 RTU Rd., Kingtown, BANGLORE 11, INDIA InvoiceDate: Jan. 1, 2016 VAT: IEA45673K. MEEE: 001234 Account No: C022123E-mail: accountsreceiveable@xyz.com Currency; EUR Phone: +123 (0)1882666. Your Ref: 123456 Fax: +123 (0)1 8821111 INVOICE TO XYZ-XYZ ITHelpdesk Block XYZ-The ABC-, cft Road-, BBM Industrial Estate- Co. IndiaIndia 13 Customer VAT No: 1E84 Line Item Description Qty Price Total 112345| XYZ BX2 Edge for Surface 10 123.48 789.60 Book at″ Smoke GraySub-Total EUR 749.60 VAT EUR 0.00 Total EUR 749.60

The knowledge extraction component 715 uses existing Optical Input:Historical corpus of email descriptions and corresponding invoicedocuments containing information related to various purchase orders andinvoices. Predefined set of entities and annotated invoice textual/htmldocuments an email description with various entities.

FIG. 9 illustrates a knowledge extraction process 900, according to anexample embodiment of the present disclosure.

Initially, a historical training set 905 of invoice textual/htmldocuments is fed into the knowledge extraction component 715. Theknowledge extraction component 715 performs the ticket sampling andsplit into various training, validation and test sets. Thereafter, theknowledge extraction component 715 prunes out very noisy and missingcontent types of invoice copies based on quality analysis and retainsthe valid invoice copies with minimal noise.

The knowledge extraction component 715 then performs right segmentationof invoice copy based on sequence of lines and separators present in theinvoice text. The knowledge extraction component 715 formulatesmeaningful chunks, also referred to as pseudo block of chunks, requiredto extract entities correctly, and denotes them as minimal sample text.Further, the knowledge extraction component 715 compiles features 910,such as embedding features (semantic features) for all the entitiespresent in the historical training set 905 using use word2vecalgorithms. In an example, the embedding dimensions may vary from50-300.

Subsequently, the knowledge extraction component 715 represents eachpseudo block of chunks into embedding semantic space to act as an inputfor deep Long Short-Term memory (LSTM) networks. Accordingly, theknowledge extraction component 715 automatically learn patterns specificto each entity from the historical training set 905, and for a giveninput invoice text, the knowledge extraction component 715 predicts theright set of entities and values with high confidence scores.

According to an example embodiment, the knowledge extraction component715 predicts the key entities and values with high-accuracy and isscalable. The knowledge extraction component 715 selects highlyconfident, mid and low confident predictions by varying confidencethreshold from 0.9 to 1 which can help the external systems to quicklytake decisions based on its confidence and provide theirrecommendations. Accordingly, with the help of minimal features, system100 can automatically infer variations of various vocabulary and canhandle unseen semantic variants present in the completely unseen emaildescriptions.

Continuing with the description of FIG. 7, an information retrievalcomponent 720 of the system 100 retrieves various types of informationfrom external Enterprise Resource Planning (ERP) systems 705 anddynamically updates these ERP systems 705. In an example, informationspecific to the invoice state and processing state are stored in the ERPsystems 705.

According to an example embodiment, the information retrieval component715 extracts data such as invoice number, purchase order number, vendorname, and vendor ID along with the ticket query type/intent from the ERPsystems 705. The extracted data is then used by the informationretrieval component 720 to query Systems Application and Products (SAP)database 725, where information about the invoices are manually enteredby the external systems. If the record is found in the SAP database 725,then it can be inferred that the invoice is already processed asexternal systems make this entry only when the invoices are processed,the information retrieval component 720 then directly responds to therequestor by noting down the invoice due date and compose an automatedemail by selecting the relevant email template.

Further, in case the invoice record is not found in the SAP database725, the information retrieval component 720 checks the invoice recordin Document Management System (DMS) 735, in certain circumstances, theinvoices get rejected. If it's a rejection case, then the rejectiondescription can be found in DMS 735. In such cases, the informationretrieval component 720 can directly select the appropriate emailtemplate with the rejection description and respond to the requestor.

According to an example implementation, an information conciliationcomponent 740 of the system 100 aggregates information from varioussources and performs intelligent reconciliation of information beforepresenting it to the end user for resolving the tickets. In an example,the information conciliation component 740 employs semantics and lexicalbased matching and ranking techniques for reconciling the information.Once the information conciliation component 740 aggregates theinformation, the information conciliation component 740 performsvalidations based on business rules and performs reconciliation.

Initially, the information conciliation component 740 is fed withknowledge extracted from email descriptions, entities, values,confidence scores, and invoice documents using statistical ML/DL methodsand business based knowledge approach as described above. Further, theinformation conciliation component 740 employs priority/weight-basedapproach on extracted entities and values from different algorithms toselect the highly confident fields using NLP/DL-based word2vec semanticsimilarity and cosine distance measures along with text fuzzy matching.In an example, the algorithm provides with matched semantic similarityscores between entities and the values. Further, the informationconciliation component 740 sets a threshold of greater than equal to0.90 (range is from 0 to 1) and retain the high confident entities andvalues among algorithms. Accordingly, the information conciliationcomponent 740 obtains consolidated entities and values and rank them inorder based on confidence scores.

Thereafter, the information conciliation component 740 applies thebusiness knowledge such as rules, conditions, and constraint on top ofranked entities and values and filters out the false positives andretains the ranked entities and values. The information conciliationcomponent 740 applies reasoning techniques. For example, if there aremultiple currency values which are extracted from the invoices. Theinformation conciliation component 740 can infer the right currency fromthe originated invoice/vendor region. Finally, the informationconciliation component 740 validates whether the extracted entities areaccurate and reliable.

According to an example embodiment, an agent validation component 745 ofthe system 100 segregates those tickets that can be resolved by thesystem 100 itself and the tickets that requires human assistance toresolve the tickets. In an example, the agent validation component 745of the system 100 employ machine learning-based probabilistic andranking approach to isolate agent-assisted and system resolved tickets.Initially, ‘n’ number of confidence scores emitted from the ML/DLalgorithms both for the ticket category/intents and for the entities andvalues, the agent validation component 745 devises the confidenceintervals based on thresholds.

Subsequently, the agent validation component 745 classifies theconfidence intervals into three predefined levels—high, mid and low. Inan example, the agent validation component 745 sets high range between0.9 and 1.0, mid-range between 0.5 and 0.9, and low-range between 0 and0.5. In an example, the agent validation component 745 estimates theconfidence intervals using trial and error method using cm′ number ofticket predictions for categories, entities and values. Using the setthresholds, the agent validation component 745 determines a total numberof tickets that are highly accurate, a total number of tickets that arepartially accurate, and a total number of tickets that are not accurate.The agent validation component 745 consider those tickets that fall in‘high’ range as potential tickets that can be resolved without humantouch and ‘mid’ range tickets as those tickets that requires human touchand ‘low’ classified tickets will not be passed for the humanverification and these tickets can be solved manually by externalsystems.

According to an example embodiment, an email resolution component 750 ofthe system 100 resolves the tickets by updating required information invarious ERP systems 705 such as CRM, SAP, etc and sends relevant emailreply by incorporating ticket problem specific information in the emailreply. In an example, the email resolution component 750 generates emailreply using advanced natural language generation and user-definedtemplate methods. According to said example, the email resolutioncomponent 750 uses ticket intent/category specific reply templates tosend a reply to the vendor by enclosing the required information for therequested ticket. The email resolution component 750 may also employML/DL based automatic email reply language generation method to composean email reply.

Initially, invoice state/status information is retrieved from the ERPsystems 705, static email reply templates, business specificrules/knowledge, taxonomy and the identified knowledge. The emailresolution component 750 may receive a ticket request from a vendor. Inan example, different types of ticket requests have different types ofcategories. Depending on the category of input request, external systemcomposes different types of email. This information is encoded intaxonomy format and constructed using business process specificknowledge. Taxonomy consists of key invoice information, state andinformation relevant to the intended vendor and right email template.

According to an example template, the email resolution component 750fills the email reply template based on taxonomy nodes and composes theemail reply with necessary information in the selected right template.The email resolution component 750 may also employ DL-based email replygeneration techniques as an alternate method for forming the emailreply. Further, in some cases, where the ticket category is not correct,then in such cases, already selected email reply template needs to bechanged/modified by the external system. If such is the case, then theemail resolution component 750 selects the dynamically right templatewithout the human touch using tree-based approach with relevantinformation.

Once the email reply is sent to the vendor with the resolutioninformation, for some type of tickets, external systems need to updatethe invoice state in the external ERP systems 705 used in ticketresolution life cycle. For highly confident tickets, the emailresolution component 750 automatically updates the information usingquerying mechanism.

In case of tickets that are of type instructive, same mechanism asdescribes above can be applied to provide the resolution. In case of thetickets that involve computation, for example ‘please perform the 2%discount on this invoice—12345’ type of requests, the system 100 employsrelevant computation on the targeted field—‘total paid amount’ usingcomputation mechanism. In case of the tickets that fall underdiscrepancies/investigations, the system 100 employs deductive andabductive kind of reasoning in addition with knowledge base and providesthe resolution. Further, the system 100 may also employ semantics-basedentailment methodology to identify the differences among variousentities and values. In case of the tickets where it is needed toprovide reason for rejections—for example ‘why this invoice is rejected,could you please provide us the reason’, the system 100 may resolve suchtypes of tickets by identifying the missing information throughinference, provide relevant rejection to the end user leveraging processspecific knowledge.

According to an example embodiment, the system 100 identifies subset ofpotential feedback documents. In an example, the system 100 employsdeep-learning based word2vec in combination with duplicate documentdetection method using K-means clustering to identify the potentialfeedback documents. In an example, the system 100 performs semanticsimilarity between existing training corpus and the input feedbackdocument, and based on similarity threshold, the system 100 identifiesthe feedback document that is fit for augmenting into the existingcorpus for re-learning. If similarity threshold is less than 0.5, thesystem 100 considers the document as a potential feedback document forre-learning.

Subsequently, for a given feedback document, invoice text document andcorresponding validated/corrected entities and values, the system 100checks whether these entities and values are missing in the trainingcorpus. If more than 50% of the entities are missing, the system 100considers corresponding invoice document for re-learning. In an example,if there are ‘n’ entities specific to that given invoice request, and90% of the entities are modified/validated, the system 100 considersthis document as potential document for re-learning

Once the potential document from the entire feedback corpus isidentified, then the system 100 corrects/annotates the correspondinginput document, email description/invoice attachment with the rightcategories, and entities and values. In case of ticket ‘category’correction, the system 100 augments the potential category document tothe existing training corpus and model can be re-learned to update thenew or modified knowledge. In case of updating ‘entities and values’ inthe corresponding invoice document, the system 100 may mark/add theright annotations based on the entity/value offsets/positions in thesequential order and preserve the right context for re-learning purpose.The system 100 may update the document for re-learning the entities andvalues. The re-learning can happen continuously as soon as there are ‘n’numbers of feedback data points available for learning. As the newtickets from the vendor varies with respect to the kind of languageentities they use, this feedback learning mechanism can help inacquiring the updated or new knowledge in specific businessprocesses/industries.

FIG. 10 illustrates a hardware platform 1000 for implementation of thesystem 100, according to an example of the present disclosure.

In an example embodiment, the hardware platform 1000 may be a computersystem 1000 that may be used with the examples described herein. Thecomputer system 1000 may represent a computational platform thatincludes components that may be in a server or another computer system.The computer system 1000 may execute, by a processor (e.g., a single ormultiple processors) or other hardware processing circuit, the methods,functions and other processes described herein. These methods, functionsand other processes may be embodied as machine readable instructionsstored on a computer readable medium, which may be non-transitory, suchas hardware storage devices (e.g., RAM (random access memory), ROM (readonly memory), EPROM (erasable, programmable ROM), EEPROM (electricallyerasable, programmable ROM), hard drives, and flash memory). Thecomputer system 1000 may include a processor 1005 that executes softwareinstructions or code stored on a non-transitory computer readablestorage medium 1010 to perform methods of the present disclosure. Thesoftware code includes, for example, instructions to preprocess theclaims, resolve exceptions, incorporate third party data, adjudicate theclaims, and validate the adjudication. In an embodiment, the advisor 120is a software code or a component performing the above steps.

The instructions on the computer readable storage medium 1010 are readand stored the instructions in storage 1015 or in random access memory(RAM) 1020. The storage 1015 provides a large space for keeping staticdata where at least some instructions could be stored for laterexecution. The stored instructions may be further compiled to generateother representations of the instructions and dynamically stored in theRAM 1020. The processor 1005 reads instructions from the RAM 420 andperforms actions as instructed.

The computer system 1000 further includes an output device 1025 toprovide at least some of the results of the execution as outputincluding, but not limited to, visual information to users. The outputdevice can include a display on computing devices. For example, thedisplay can be a mobile phone screen or a laptop screen. GUIs and/ortext are presented as an output on the display screen. The computersystem 1000 further includes input device 1030 to provide a user oranother device with mechanisms for entering data and/or otherwiseinteract with the computer system 1000. The input device may include,for example, a keyboard, a keypad, a mouse, or a touchscreen. In anembodiment, recommendations from the advisor 120 are displayed on theoutput device 1025. Each of these output devices 1025 and input devices1030 could be joined by one or more additional peripherals.

A network communicator 1035 may be provided to connect the computersystem 1000 to a network and in turn to other devices connected to thenetwork including other clients, servers, data stores, and interfaces,for instance. A network communicator 1035 may include, for example, anetwork adapter such as a LAN adapter or a wireless adapter. Thecomputer system 1000 includes a data source interface 1040 to accessdata source 1045. A data source is an information resource.

FIG. 11 illustrates a computer-implemented method 1100 depictingfunctionality of the system 100, according to an example embodiment ofthe present disclosure. For the sake of brevity, construction andoperational features of the system 100 which are explained in detail inthe description of FIG. 1-FIG. 7 are not explained in detail in thedescription of FIG. 8.

At method block 1105, the method 1100 commences with receiving a servicerequest from a client. In an example, the service request is indicativeof a service task to be performed and information associated with theservice task.

At method block 1110, the service request is processed to generate anintermediate service response corresponding to the service request. Theintermediate service response is generated as an intermediaterecommendation.

At method block 1115, a confidence level associated with theintermediate service response is determined.

At method block 1120, it is ascertained whether the confidence levelassociated with the service response is below a pre-determined thresholdlevel. On ascertaining that the confidence level is above thepre-determined threshold level (‘YES’), at method block 1125, theintermediate recommendation is finalized based on validation from anexternal system.

Further, on ascertaining that the confidence level is below thepre-determined threshold level (‘NO’), at method block 1130, trainingdata is extracted from a database. The training data includes externalsystem inputs and corresponds to the service request.

At method block 1135, a final service response is generatedcorresponding to the service request based on the training data. Thefinal service response is generated as a final recommendation. In anexample, this process is continued until the confidence level is reachedor a number of iterations have reached. This is usually applicable tomore difficult tasks as there may be insufficient training data.

What has been described and illustrated herein are examples of thepresent disclosure. The terms, descriptions and figures used herein areset forth by way of illustration only and are not meant as limitations.Many variations are possible within the spirit and scope of the subjectmatter, which is intended to be defined by the following claims andtheir equivalents in which all terms are meant in their broadestreasonable sense unless otherwise indicated.

What is claimed is:
 1. A system comprising: a curator to capture inputdata corresponding to a plurality of service tasks from an externalsource; a browser extension, in communication with the curator, thebrowser extension to collect intermediate service delivery data from theexternal source; a learner, in communication with the browser extension,the learner to store the input data and the intermediate servicedelivery data as training data; a receiver, in communication with thelearner, the receiver to receive a service request from a client,wherein the service request is indicative of a service task to beperformed and information associated with the service task; and anadvisor, in communication with the receiver, the advisor to: process theservice request to generate an intermediate service responsecorresponding to the service request, wherein the intermediate serviceresponse is generated as an intermediate recommendation; determine aconfidence level associated with the intermediate service response;ascertain whether the confidence level associated with the serviceresponse is below a pre-determined threshold level; and on ascertainingthat the confidence level is below the pre-determined threshold level,generate a final service response corresponding to the service requestbased on the training data, wherein the final service response isgenerated as a final recommendation.
 2. The system as claimed in claim1, wherein the input data includes question-answers pairs and dialogtraces corresponding to each of the plurality of service tasks.
 3. Thesystem of claim 1, wherein the advisor is to determine the confidencelevel associated with the intermediate service response based on aconfusion matrix.
 4. The system of claim 1, wherein the advisor furtheris to, on ascertaining that the confidence level associated with theservice response is above the pre-determined threshold level, finalizethe intermediate recommendation based on validation from an externalsystem.
 5. The system as claimed in claim 1, wherein the system istrained with the input data and the intermediate service delivery datausing machine learning techniques.
 6. A system comprising: a receiver toreceive a service request from a client, an advisor, in communicationwith the receiver, the advisor to: process the service request togenerate an intermediate service response corresponding to the servicerequest, wherein the intermediate service response is generated as anintermediate recommendation; determine a confidence level associatedwith the intermediate service response; ascertain whether the confidencelevel associated with the service response is below a pre-determinedthreshold level; on ascertaining that the confidence level is below thepre-determined threshold level, extract training data from a database,wherein the training data corresponds to the service request; andautomatically generate a final service response corresponding to theservice request based on the training data, wherein the final serviceresponse is generated as a final recommendation.
 7. The system of claim6, wherein the service request is indicative of a service task to beperformed and information associated with the service task.
 8. Thesystem of claim 6, wherein the advisor determines the confidence levelassociated with the intermediate service response based on a confusionmatrix.
 9. The system of claim 6, wherein the training data isindicative of data gathered from external sources.
 10. The system ofclaim 6, wherein the advisor further is to, on ascertaining that theconfidence level associated with the service response is above thepre-determined threshold level, finalize the intermediate recommendationbased on validation from an external system.
 11. The system of claim 6,wherein the system further comprises: a curator to capture input datacorresponding to a plurality of service tasks from an external source; abrowser extension, in communication with the curator, the browserextension to collect intermediate service delivery data from theexternal source; and a learner, in communication with the browserextension, the learner to store the input data and the intermediateservice delivery data as the training data.
 12. The system as claimed inclaim 11, wherein the input data includes question-answers pairs anddialog traces corresponding to each of the plurality of service tasks.13. The system as claimed in claim 6, wherein the system is trained withthe training data using machine learning techniques.
 14. Acomputer-implemented method, executed by at least one processor, themethod comprising: receiving a service request from a client, processingthe service request to generate an intermediate service responsecorresponding to the service request, wherein the intermediate serviceresponse is generated as an intermediate recommendation; determining aconfidence level associated with the intermediate service response;ascertaining whether the confidence level associated with the serviceresponse is below a pre-determined threshold level; on ascertaining thatthe confidence level is below the pre-determined threshold level,extract training data from a database, wherein the training datacorresponds to the service request; and automatically generate a finalservice response corresponding to the service request based on thetraining data, wherein the final service response is generated as afinal recommendation.
 15. The computer-implemented method of claim 14,wherein the service request is indicative of a service task to beperformed and information associated with the service task.
 16. Thecomputer-implemented method of claim 14, wherein the confidence levelassociated with the intermediate service response based on a confusionmatrix.
 17. The computer-implemented method of claim 14, wherein thetraining data is indicative of data gathered from external sources. 18.The computer-implemented method of claim 14, wherein the on ascertainingthat the confidence level associated with the service response is abovethe pre-determined threshold level, the intermediate recommendation isfinalized based on validation from an external system.
 19. Thecomputer-implemented method of claim 14 further comprising: capturinginput data corresponding to a plurality of service tasks from anexternal source; collecting intermediate service delivery data from theexternal source; and storing the input data and the intermediate servicedelivery data as the training data.
 20. The computer-implemented methodof claim 19, wherein the input data includes question-answers pairs anddialog traces corresponding to each of the plurality of service tasks.